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Abstract #3269

Automatic segmentation of arterial vessel wall on undersampled MR image using deep learning

Shuai Shen1,2,3,4, Xiong Yang5, Jin Fang6, Guihua Jiang6, Shuheng Zhang5, Yanqun Teng5, Xiaomin Ren5, Lele Zhao5, Jiayu Zhu5, Qiang He5, Hairong Zheng1,3,4, Xin Liu1,3,4, and Na Zhang1,3,4
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, 2College of Software, Xinjiang University, Urumqi,, China, 3Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, 4CAS key laboratory of health informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, 5Shanghai United Imaging Healthcare Co., Ltd., shanghai, China, 6Department of Radiology, Guangdong Second Provincial General Hospital, guangdong, China

A total of 124 patients were included in this study. We used U-net neural network architecture to segment the arterial vessel wall on original acquired MR vessel wall images and the corresponding images reconstructed from undersampled K-space data. The Dice coefficients based on the original K-space data, the K-space data with a sampling rate of 7.7%, and K-space data with a sampling rate of 1.9% were 88.66%, 88.19%, and 87.66%, respectively. The effectiveness of arterial vessel wall segmentation on undersampled images using U-net network was verified. The result demonstrated the potential to improve the acceleration performance of MR imaging.

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